To solve this problem, we can use the information provided in the ANOVA table, particularly the standard error of the estimate (which is 7.37). Since about 95% of the residuals are expected to fall within ±1.96 standard deviations (based on the properties of a normal distribution), we will compute the following:

$\text{Range of residuals} = \pm 1.96 \times \text{Standard Error of Estimate}$

Let's calculate this:

$1.96 \times 7.37 = 14.44$

Thus, about 95% of the residuals will be between:

$\text{Lower bound} = -14.44, \quad \text{Upper bound} = 14.44$

So, the residuals will fall between -14.44 and 14.44.

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Would you like any further details or have any questions?

Here are 5 related questions you might find interesting:

1. How is the standard error of the estimate calculated in regression analysis?
2. Why do we use 1.96 standard deviations for a 95% confidence interval?
3. What is the relationship between residuals and the accuracy of a regression model?
4. How does an increase in residual variance affect the model's predictions?
5. How would you interpret an ANOVA table with a different F-value?

Tip: Always check the assumptions of normality and homoscedasticity when interpreting residuals in regression models.